9 accepted papers
Noisy correspondence in cross-modal retrieval introduces significant challenges due to its inherent difficulty in identification and correction. Although existing methods attempt to minimize the influence of noisy samples by the weighting mechanism, these methods still struggle with performance degr
Exploring multimodal information from external knowledge bases in Visual Question Answering (VQA) reasoning tasks presents a significant challenge. Current methods, such as attention-based and graph-based approaches, have limitations in effectively capturing contextual key information. It is essenti…
Few-shot learning has attracted considerable attention in the field of hyperspectral image (HSI) classification due to its suitability in addressing the challenges encountered in numerous real-world scenarios. However, the scarcity of labeled samples poses a significant challenge in learning informa…
Cross-modal retrieval with deep neural networks heavily relies on accurate annotation. However, existing methods may easily suffer from the scarcity and validity of annotations due to the expensive cost of manual labeling. In addition, it is inevitable that noisy labels are imposed during labeling.…
Few-shot segmentation aims at segmenting target object areas with only a few labeled samples. Previous methods extract class-specific prototypes to guide segmentation. How-ever, using one or more prototypes to represent the whole object inevitably drops vital spatial information, ignoring many detai…
Few-shot learning (FSL) aims to generalize from few labeled samples. Recently, metric-based methods have achieved surprising classification performance on many FSL benchmarks. However, those methods ignore the impact of noise, making the few-shot learning still tricky. In this work, we identify that…
Generalized zero-shot learning (GZSL) has attracted considerable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes. Most of the existing methods attempt to find a mapping from visual space to semantic space, such mapping can easil…
In recent years, hashing based cross-modal retrieval methods have attracted considerable attention for the high retrieval efficiency and low storage cost. However, most of the existing methods neglect the high-order relationship among data samples. In addition, most of them can only deal with two mo…
Hashing technique has been widely applied to large-scale image retrieval due to its efficacy in storage and retrieval. However, due to the explosive growth of multimedia data on the web, existing hashing approaches can hardly achieve satisfactory performance on the newly-emerging images of new class…